When compared to the direct observation of scenes, color images in general have two major limitations due to scene lighting conditions. First, the images captured and displayed by photographic and electronic cameras suffer from a comparative loss of detail and color in shadowed zones. This is known as the dynamic range problem. Second, the images are subject to color distortions when the spectral distribution of the illuminant changes. This is known as the color constancy problem. (Note that for non-color imaging including non-optical imaging, the problem becomes simpler and is largely one of dynamic range compression, i.e., the capture and representation of detail and lightness values across wide ranging average signal levels that can vary dramatically across a scene.)
Electronic cameras (e.g., cameras based on CCD detector arrays, CMOS technology, etc.) are capable of acquiring image data across a wide dynamic range. This range is suitable for handling most illumination variations within scenes, and lens aperture changes are usually employed to encompass scene-to-scene illumination variations. Typically though, this dynamic range is lost when the image is digitized or when the much narrower dynamic range of print and display media are encountered. For example, most images are digitized to 8-bits/color band (256 gray levels/color band) and most display and print media are even more limited to a 50:1 dynamic range.
A commonly encountered instance of the color constancy problem is the spectral difference between daylight and artificial (e.g., tungsten) light which is sufficiently strong to require photographers to shift to some combination of film, filters and processing to compensate for the spectral shift in illumination. Though film photographers can attempt to approximately match film type to spectral changes in lighting conditions, digital cameras must rely strictly on filters. However, these methods of compensation do not provide any dynamic range compression thereby causing detail and color in shadows to be lost or severely attenuated compared to what a human observer would actually see.
Another problem encountered in color and non-color image processing is known as color/lightness rendition. This problem results from trying to match the processed image with what is observed and consists of 1) lightness and color “halo” artifacts that are especially prominent where large uniform regions of an image abut to form a high contrast edge with “graying” in the large uniform zones, and 2) global violations of the gray world assumption (e.g., an all-red scene) which results in a global “graying out” of the image.
Since human vision does not suffer from these various imaging drawbacks, it is reasonable to attempt to model machine vision based on human vision. A theory of human vision centered on the concept of a center/surround retinex was introduced by Edwin Land in “An Alternative Technique for the Computation of the Designator in the Retinex Theory of Color Vision,” Proceedings of the National Academy of Science, Volume 83, pp. 3078-3080, 1986. Land drew upon his earlier retinex concepts disclosed in “Color Vision and The Natural Image,” Proceedings of the National Academy of Science, Volume 45, pp. 115-129, 1959, but harmonized these with certain findings of the neurophysiology of vision. All of the retinex concepts were intended to be models for human color perception. The earlier retinex concepts involved “random walks” across image space and the resetting of the computation when color boundaries were crossed. Land's 1986 retinex concept of human vision was proposed as a center/surround spatial computation where the center was 2-4 arc-minutes in diameter and the surround was an inverse square function with a diameter of about 200-250 times that of the center.
The application of Land's human vision theories to image processing has been attempted in the prior art. For example, to mimic the dynamic range compression of human vision, a detector array with integrated processing in analog VLSI silicon chips used a logarithm transformation prior to the surround formation. See “Analog VLSI and Neural Systems,” C. Mead, Addison-Wesley, Reading, Mass., 1989. In an attempt to improve color constancy, the implementation of a color retinex in analog VLSI technology is suggested by Moore et al., in “A Real-time Neural System for Color Constancy,” IEEE Transactions on Neural Networks, Volume 2, pp. 237-247, March 1991. In Moore et al., the surround function was an exponential and final processing before display of the image required the use of a variable gain adjustment that set itself by finding the absolute maximum and minimum across all three color bands' signal values. However, none of the above-described prior art provided an image processing technique that could simultaneously accomplish/improve dynamic range compression, color independence from the spectral distribution of the scene illuminant, and color/lightness rendition.
To address these issues, U.S. Pat. No. 5,991,456 discloses a method of improving a digital image in which the image is initially represented by digital data indexed to represent positions on a display. The digital data is indicative of an intensity value Ii(x,y) for each position (x,y) in each i-th spectral band. The intensity value for each position in each i-th spectral band is adjusted to generate an adjusted intensity value for each position in each i-th spectral band in accordance with             ∑              n        =        1            N        ⁢                  W        n            ⁢              (                              log            ⁢                                                   ⁢                                          I                i                            ⁢                              (                                  x                  ,                  y                                )                                              -                      log            ⁢                                                   [                                                            I                  i                                ⁢                                  (                                      x                    ,                    y                                    )                                            *                                                F                  n                                ⁢                                  (                                      x                    ,                    y                                    )                                                      ]                          )              ,      i    =    1    ,  …  ⁢           ,  Swhere Wn is a weighting factor, “*” is the convolution operator and S is the total number of unique spectral bands. For each n, the function Fn(x,y) is a unique surround function applied to each position (x,y) and N is the total number of unique surround functions. Each unique surround function is scaled to improve some aspect of the digital image, e.g., dynamic range compression, color constancy, and lightness rendition. The adjusted intensity value for each position in each i-th spectral band is filtered with a common function. The improved digital image can then be displayed and is based on the adjusted intensity value for each i-th spectral band so-filtered for each position. For color images, a color restoration step can be added to give the image true-to-life color that closely matches human observation.
While this patented method performs well for scenes/images having widely varying lighting, reflectance and/or topographic features (referred to hereinafter as wide dynamic range images), the method provides a lesser degree of improvement for scenes/images having constrained lighting, reflectance and/or minimal topographic variations (referred to hereinafter as narrow dynamic range images). Furthermore, it has been found that the use of this patented method can cause large “white” zones in digital images to be “grayed”. The larger and more constant the white zone, the greater the degree of graying. Such white zones are commonly found in artificial images generated by both computer graphics and document imaging applications.